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A predictive model for respiratory distress in patients with COVID-19: a retrospective study
Xin Zhang1,2,3,4, Wei Wang5, Cheng Wan1,3
1Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Insights
A predictive model identifies five key factors at admission to forecast respiratory distress in COVID-19 patients. This tool aids early risk stratification and effective resource allocation for better patient outcomes.
Area of Science:
- Medical research
- Clinical informatics
- Epidemiology
Background:
- Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, presents a significant global health challenge.
- A retrospective study analyzed 863 hospitalized patients with COVID-19 (IWCH-COVID-19).
Purpose of the Study:
- To develop a predictive model for identifying patients at risk of respiratory distress within 30 days of admission.
- To identify key risk factors associated with the development of respiratory distress in COVID-19 patients.
Main Methods:
- Kaplan-Meier and Cox proportional hazards analyses were employed to evaluate risk factors.
- A predictive model was constructed using significant variables.
- Model performance was assessed using C-statistics and confidence intervals.
Main Results:
- Admission factors positively associated with respiratory distress included high neutrophil count (>6.3×10⁹/L), elevated D-dimer (≥1.00 mg/L), and fever (≥37.3 °C).
- Factors negatively associated with respiratory distress were specific ranges of Complement C3 (0.9-1.8 g/L) and platelet counts (>350×10⁹/L or 125-350×10⁹/L).
- The final five-factor model demonstrated strong predictive performance with a C statistic of 0.891.
Conclusions:
- A validated predictive model using five admission factors can accurately assess respiratory distress risk in COVID-19 patients.
- Early patient classification allows for timely, effective treatment and optimized health resource allocation.
- This model supports proactive management strategies for hospitalized COVID-19 patients.
Background:
Coronavirus disease 2019 (COVID-19), associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global public health crisis. We retrospectively evaluated 863 hospitalized patients with COVID-19 infection, designated IWCH-COVID-19.
Methods:
We built a successful predictive model after investigating the risk factors to predict respiratory distress within 30 days of admission. These variables were analyzed using Kaplan-Meier and Cox proportional hazards (PHs) analyses. Hazard ratios (HRs) and performance of the final model were determined.
Results:
Neutrophil count >6.3×109/L, D-dimer level ≥1.00 mg/L, and temperature ≥37.3 °C at admission showed significant positive association with the outcome of respiratory distress in the final model. Complement C3 (C3) of 0.9-1.8 g/L, platelet count >350×109/L, and platelet count of 125-350×109/L showed a significant negative association with outcomes of respiratory distress in the final model. The final model had a C statistic of 0.891 (0.867-0.915), an Akaike's information criterion (AIC) of 567.65, and a bootstrap confidence interval (CI) of 0.866 (0.842-0.89). This five-factor model could help in early allocation of medical resources.
Conclusions:
The predictive model based on the five factors obtained at admission can be applied for calculating the risk of respiratory distress and classifying patients at an early stage. Accordingly, high-risk patients can receive timely and effective treatment, and health resources can be allocated effectively.
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